Neural Network solvers for the Yang-Baxter Equation

Abstract: We develop a novel neural network architecture that learns solutions to the Yang Baxter equation for R matrices of difference form. This method already enables us to learn all solution classes of the 2d Yang Baxter equation. We propose and test paradigms for exploring the landscape of Yang Baxter equation solution space aided by these methods. Further, we shall also comment on the application of these methods to generating new solutions of the Yang Baxter equation. The talk is based on joint work with Suvajit Majumder and Evgeny Sobko available in part in arXiv:2304.07247.

machine learningmathematical physicscommutative algebraalgebraic geometryalgebraic topologycombinatoricsdifferential geometrynumber theoryrepresentation theory

Audience: researchers in the topic

Machine Learning Seminar

Series comments: Online machine learning in pure mathematics seminar, typically held on Wednesday. This seminar takes place online via Zoom.

For recordings of past talks and copies of the speaker's slides, please visit the seminar homepage at: kasprzyk.work/seminars/ml.html